1. Field of the Invention
The present invention generally relates to electro-optical systems and, more particularly, analyzing and reducing data generated by said systems.
2. Description of the Related Art
The most basic of electro-optical sensors are those that measure intensity. They are often known in the visible imaging community as black and white sensors. It is known that adding color information to that type of sensor creates more information. In terms of human viewing of the measured scene, a color image is more pleasing to the eye than the intensity based black and white image. Color sensing devices for both still cameras and television cameras generally measure 3 colors: red, green, and blue. In these imagers, only the three independent colors are recorded and the color information displayed is some combination of the three colors. However, for many applications, it is necessary to record many more independent colors. Color information also exists in optical bands from ultraviolet (0.3 μm) through infrared (20 μm). Outside of this region there is additional information, but the atmosphere usually absorbs the energy making it challenging to record any useful information. An additional complication arises with the measurement of photon energy at wavelengths beyond 1.7 μm. Contemporary sensing materials that can be used for these wavelengths require some form of external cooling. The addition of a cooling requirement adds considerable expense, complexity and reduction of reliability to any imaging system. Contemporary earth-based and satellite-based imaging systems generally use many more than three spectral bands for imaging tasks. The spectral sensing community defines systems which measure more than 100 colors as hyperspectral.
The complexity of the data measured by a high fidelity hyperspectral imaging system is shown in FIG. 1. This image was generated by NASA/JPL and illustrates the result of the measured data as a hyperspectral image cube 10. The data was taken from an aircraft flying at 20,000 feet. The top 12 of the cube 10 is a black and white representation of a false color image that is human friendly and shows the various recorded land features. The vertical axis 14 of this image cube is the color dimension, which contains 224 independent colors from the visible spectral band (0.4 μm) to the Short Wave Infrared (2.4 μm). X and Y dimensional data are along axes 16 and 18 respectively. The added variable of time evolution increases the dimensionality of the data needed to analyze to four (x-position, y-position, spectrum, time), thus increasing the complexity.
However, the complexity may be decreased by noting that each pixel in the data cube 10 of FIG. 1 has 244 spectral components. Hypercubes such as that shown in FIG. 1 may be important for understanding plant and crop physiology. By limiting analysis to single pixels, the data complexity may be reduced to three (one pixel, color and time). If the analysis is further limited to cases where there is only a single constituent in each pixel, it is not necessary to confront the problem of mixed pixels where there are a mixture of items in each pixel. A typical spectrum 20 from a single pixel is shown in FIG. 2. A different phenomenon is shown in FIG. 3. The data is from a land based spectral instrument and consists of 180 individual wavelengths covering the visible (0.5 μm) through the (5.5 μm) in the Mid Wave Infrared (MWIR) spectral region recorded at a single instant of time. In order to assess the time dimension or the time evolution of spectrum, refer to graph 22 in FIG. 3. The graph 22 shows the image of a star looking through the atmosphere where there is significant turbulence. The three curves are taken at three successive instants of time spaced two milliseconds apart. There are about 100 separate independent spectral measurements displayed on this graph. The transient spectral character is displayed by the intensity at each two ms time sample. In the visible/near infrared portion of the spectrum (0.7 μm to 0.9 μm) the signal goes from being in the noise (15 data counts) at t=0 to 85 data counts four milliseconds later.
Accordingly, even with the reduced complexity, there is a need in the art for a method and apparatus for more efficiently examining complex spectral data sets.